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The 365 Team 11 Apr 2024 7 min read
If you’re aiming for a data analyst job, sooner or later, you’ll reach the final stage of the application process: the interview. But how can you ace the interview? Be well-acquainted with the interview questions for data analysts in advance.
This article addresses everything necessary to nail the challenging job interview and secure a career as a data analyst, including the following:
No matter where you apply for a data analyst job, no recruiter will call you for an interview if you don’t possess the following necessary skills:
Unsure how to prepare for a data analyst interview?
Take the time to grow your knowledge, pen down possible entry-level data analyst questions and answers, and soon enough, you’ll feel more comfortable with those topics. Ensure you have what it takes to ace your SQL interview questions for a data analyst and all other data analyst technical interview questions, and you can expect great results in the long run. And remember—while preparing for an entry-level interview, you should find a way to highlight how a combination of your practical knowledge and your creativity will add value to your prospective employer.
Many companies may have surprising questions for data analyst interviews. The interview questions are about more than your background and work experience. Interviewers might require details about projects you’ve been involved in and how you approach complex datasets. So, let’s take a look:
Working with large datasets and dealing with many variables and columns is essential for many hiring managers. You don’t need to reveal background information about your projects or how you managed each stage. Focus on the size and type of data.
The largest dataset I’ve worked with was a joint software development project. It comprised over a million records and 600 to 700 variables. My team and I needed to work with marketing data, which we later loaded into an analytical tool to perform EDA.
Hiring managers must choose a data analyst who is knowledgeable and confident enough to initiate a change that would improve the company’s status quo. When discussing your recommendation, give as many details as possible—including your reasoning. Even if your proposal was not implemented, it demonstrates that you’re driven and strive for improvement. This may not seem like an essential data analyst job interview question, but the insights it reveals are vital for the prospective employer.
Although data analysts typically handle data from non-technical departments, I’ve worked for a company where colleagues who were not on the data analysis side had access to data. This generated many cases of misinterpreted data that caused significant damage to the overall company strategy. I gathered examples and pointed out that working with data dictionaries can do more harm than good. I recommended that my co-workers depend on data analysts for data access. Once we implemented my recommendation, the cases of misinterpreted data dropped drastically.
Working with numbers is one of many aspects of a data analyst job. Data analysts also need strong writing skills to efficiently present the results of their analysis to management and stakeholders. If you think you could be a better data storyteller, ensure you’re making efforts in that direction, e.g., via additional training.
I can interpret data clearly and concisely. I’ve had plenty of opportunities to enhance my writing skills through email communication with co-workers and writing analytical project summaries for upper management. And I’m constantly looking for further improvement in my writing skills.
Surveys have quantitative and qualitative questions, so merging those two data types presents no challenge. Data analysts must use the quantitative and qualitative data to conduct meaningful analyses. In other cases, a data analyst must use creativity to find matching qualitative data. When answering this data analyst interview question, discuss the project requiring the most creative thinking.
I’ve performed a few analyses with qualitative survey data at my disposal. But I realized I could enhance the validity of my recommendations by also implementing valuable data from external survey sources. So, I used quantitative data from our distributors for a product development project, which yielded excellent results.
Employers are looking for candidates with brilliant analytical skills and the confidence and eloquence to present their results to different audiences—including upper-level management, executives, and non-technical co-workers. Strong presentation skills are asked about even in entry-level data analyst interview questions. When talking about the audiences you’ve presented to, make sure you mention the following:
In my role as a Data Analyst, I’ve presented to various audiences made up of co-workers and clients with different backgrounds. I’ve given presentations to small and more significant groups. The largest so far has been around 30 people—primarily colleagues from non-technical departments. All these presentations were in-person, except for one remote video conference call with senior management.
This question assesses if you have industry-specific skills and experience. Even if you don’t, ensure you have the proper data analyst interview preparation in advance, where you explain how you can apply your background skills from a different field to benefit the company.
As a data analyst with a financial background, there are a few similarities between this industry and healthcare. The most prominent one is data security. Both industries utilize sensitive personal data that must be kept secure and confidential. This leads to more restricted access to data and, consequently, more time to complete its analysis. I’ve learned to be more time efficient when passing through all the security. Moreover, I understand how important it is to clearly state the reasons behind requiring specific data for my analysis.
Hiring managers appreciate candidates serious about advancing their career options via additional qualifications. Certificates prove you’re eager to master new skills and gain knowledge of the latest analytical tools and subjects. While answering this question, list the credentials you’ve acquired and briefly explain how they’ve helped you boost your data analyst career. If you haven’t earned any certifications, mention the ones you’d like to work towards and why.
I’m always looking for ways to upgrade my analytics skillset, so I recently earned a certification in customer analytics in Python. The training and requirements to finish it helped me sharpen my skills in analyzing customer data and predicting the purchase behavior of clients.
A technical data analyst interview question assesses your proficiency in analytical software, visualization tools, and scripting languages, such as SQL and Python. You might be requested to answer more advanced statistical questions depending on the job specifics.
Although you might think you need experience with as many tools as possible to ace this question, this is not true. Each company uses specific data analysis tools, so it’s expected that your expertise is limited to those. Of course, if you’ve worked for many companies, you’re bound to have exposure to a wider variety of analytical software. But the interviewer wants to know which tools you feel comfortable with rather than how many you’ve utilized.
Be ready to answer specific data analyst technical interview questions—research to discover what tools are worth mentioning to the prospective employer.
When it comes to data analysis tools, I’m a traditionalist. That’s why I find Microsoft Excel and Microsoft Access most useful. I feel genuinely comfortable working with those; they’re available in almost every company. Moreover, with the proper training, you can achieve excellent results with them.
As a data analyst, you don’t specifically need experience with statistical models unless it’s required for the job you’re applying for. If you haven’t been involved in building, using, or maintaining statistical models, be open about it and mention any knowledge or partial experience you may have.
I haven’t had direct experience building statistical models as a data analyst. But I’ve helped the statistical department by ensuring they can access and analyze the correct data. The model in question was created to identify the customers most inclined to buy additional products and predict when they would make that decision. My job was to establish the appropriate variables used in the model and assess its performance once it was ready.
It's normal for a data analyst to prefer specific tasks over others. But you’ll probably be expected to deal with all project steps—from querying and cleaning through analyzing to communicating findings. So, don’t show aversion to any of the above. Instead, use this data analyst interview question to highlight your strengths. Focus on the task you like performing the most and explain why it’s your favorite.
If I had to select one step as a favorite, it would be analyzing the data. I enjoy developing a variety of hypotheses and searching for evidence to support or refute them. While following my analytical plan, I sometimes stumbled upon interesting and unexpected findings from the data. There’s always something to be learned from the big or small data that will help me in future analytical projects.
Data analysts should have basic statistics knowledge and experience. That means you should be comfortable calculating mean, median, and mode and conducting significance testing. In addition, you must be able to interpret the above in connection to the business. If a higher level of statistics is required, it will be listed in the job description.
I’ve used basic statistics in my work—mainly calculating the mean and standard variances and significance testing. The latter helped me determine the statistical significance of measurement differences between two populations for a project. I’ve also determined the relationship between two variables in a dataset, working with correlation coefficients.
Most large companies work with numerous scripting languages. So, a good command of more than one is a plus. Nevertheless, if you aren’t familiar with the primary language used by the company you apply to, you can still make a good impression. Demonstrate enthusiasm to expand your knowledge and point out that your fluency in other scripting languages gives you a solid foundation for learning new ones.
SQL for data analysts is like a chef’s knife for cooks—an essential tool that requires skills to wield effectively. The same goes for Python. So, ensure you have the knowledge to adequately demonstrate your expertise in this domain.
I’m most confident in using SQL since that’s the language I’ve worked with throughout my data analyst experience. I also have a basic understanding of Python and have recently enrolled in a Python programming course to sharpen my skills. So far, I’ve discovered that my expertise in SQL helps me quickly advance in Python.
SQL is considered one of the easiest scripting languages to learn. If you wish to be competitive in the job market as a data analyst, you should demonstrate an excellent command of SQL. Even if you don’t have years of experience, highlight how your skills have improved with each new project.
I’ve used SQL in at least 80% of my projects for five years. Of course, I’ve also turned to other programming languages for the different phases of my projects. But, all in all, it’s SQL that I’ve utilized the most and consider the best for most of my data analyst tasks.
If you’re an Excel expert, listing all the functions you’ve used would be difficult. Instead, highlight your advanced skills, such as working with statistical functions, pivot tables, and graphs. If you have experience utilizing the more challenging functions, hiring managers will presume you have experience using the more basic ones. Prepare to tackle formidable data analyst technical interview questions, so bring your A-game. Of course, if you lack the background, it’s worth considering specialized Excel training that will help you build a competitive skillset.
I’ve used Excel every day of my data analyst career in every phase of my analytical projects. For example, I’ve checked, cleaned, and analyzed datasets using pivot tables. I’ve also used statistical functions to calculate standard deviations, correlation coefficients, etc. And the Excel graphing function is excellent for developing visual summaries of the data.
I’ve worked with raw data from external vendors in many customer satisfaction surveys. First, I’d use sort functions and pivot tables to ensure the data was clean and loaded correctly. In the analysis phase, I’d segment the data with pivot tables and statistical functions if necessary. Finally, I’d build tables and graphs for efficient visual representation.
Dashboards are essential for managers because they visually capture KPIs and metrics and help them track business goals. Data analysts are often involved in building and updating dashboards. Some of the best tools for this purpose include Excel, Tableau, and Power BI. When you talk about your experience, outline the types of data visualizations and metrics you used in your dashboard.
I’ve created dashboards related to customer analytics in Power BI and Excel. I operated with pie charts, bar graphs, line graphs, and tables to visualize the data. That means I used marketing metrics, such as brand awareness, sales, and customer satisfaction.
To answer the behavioral data analyst interview question effortlessly, you’ll need to recall details about how you handled specific challenges in your work with stakeholders, coworkers, or clients.
Data analysts often need help communicating findings to co-workers from different departments or senior management with a limited understanding of data. This requires excellent skills in interpreting specific terms using non-technical language. Moreover, it also demands extra patience to listen to your co-workers' questions and provide answers in an easy-to-digest manner. Show the interviewer that you can work efficiently with people from different backgrounds.
In my work with stakeholders, it often comes down to the same challenge—facing a question I don’t have the answer to due to limitations of the gathered data or the database structure. In such cases, I analyze the available data to deliver solutions to the most closely related questions. Then, I give the stakeholders a basic explanation of the current data limitations and propose developing a project that would allow us to gather the unavailable data in the future. This shows that I care about their needs and am willing to go the extra mile to provide them with what they need.
When starting an analysis, most data analysts have a rough prediction of the outcome rested on findings from previous projects. But there’s always room for surprise, and sometimes the results are entirely unexpected. This data analyst interview question lets you discuss the analytical projects you’ve been involved in and allows you to demonstrate your excitement about drawing new developments from your projects. And don’t forget to mention the action you and the stakeholders took due to the unexpected outcome.
While performing routine customer database analysis, I was astonished to discover a customer subsegment that the company could target with a new suitable product and a relevant message. That presented an excellent opportunity for additional revenue for the company by utilizing a subset of an existing customer base. Everyone on my team was pleasantly surprised, and soon enough, we began devising strategies with Product Development to address the needs of this newly discovered subsegment.
A data analyst is typically known as a professional with a technical background and excellent math and statistical skills. But even though creativity is not the first data analyst quality that comes to mind, it’s still essential in developing analytical plans and visualizations and finding unorthodox solutions to data issues. So, provide an answer with examples of your out-of-the-box thinking.
Creativity can make all the difference in a data analyst’s work. It has helped me find intriguing ways to present analysis results to clients and devise new data checks that identify issues leading to anomalous results.
This is one of the most essential data analyst interview questions that can make or break it for you. Remember that the hiring manager wants to hear something more than “communication skills.” Think of an approach you’ve used as a data analyst to improve the quality of work in a cross-functional team.
The role of a data analyst goes beyond explaining technical terms in non-technical language. I always strive to gain a deeper understanding of the work of my colleagues so that I can bridge my explanation of statistical concepts to the specific parts of the business they deal with and show how these concepts relate to the tasks they need to solve.
Soft (non-technical) skills are vital for working efficiently with others and maintaining high performance. As with most professions, data analysts should know how their behavior and work habits affect their team members. Therefore, base your answer on past work experience and highlight an essential soft skill you have developed.
Leadership skills are one of the primary soft skills a data analyst should develop. Leadership means taking action to guide and help your team members. This doesn’t necessarily mean you need to be in a managerial position. In my work, leadership would translate into providing expert insights regarding company data and its interpretation—a skill I’ve worked hard to develop over the years. Being confident in my abilities has established me as a leading figure in my area, and my team members know they can rely on my expertise.
Interviews for analytical and technical positions often include brainteasers that evaluate how you apply logic, critical thinking, and creativity under pressure.
You need to create the following equation. The total distance that needs to be traveled both ways is 120 miles. The average speed that we need to maintain is 40 mph; therefore, the car will travel for 3 hours—e.g.:
The car has already traveled for two hours:
The distance is 60 miles. So, the car must travel at 60 mph for only 1 hour on the way back.
The first number in the sequence is 2.
The second number is 6, which is obtained by summing the previous number (2) with the addend 4.
The third number in the sequence is 12, obtained by taking the sum of the previous number (6) with the addend from the previous step increased by 2. That is:
The fourth number is 20, calculated analogously by taking the sum of the previous number in the sequence and the addend from the last step increased by 2, namely:
If we continue this pattern—adding a number that increases by 2 with each step (4, 6, 8, . ) —the next addend would be 8 + 2 = 10. Therefore, to find the fifth number in the series, add 10 to the fourth number in the sequence: 20 +10 = 30.
So, the next number in the series is 30.
Guesstimates can be critical in picking the right candidate for a data analyst job because they assess your problem-solving abilities, confidence with numbers, and how you handle different scenarios.
With such data analyst job interview questions, employers test your ability to think independently. Choose a small family restaurant (not a chain), making calculations more manageable. Then define the main aspects of the restaurant—e.g.:
Suppose the restaurant is open six days a week (closed on Mondays)—i.e., it’s open 25 times per month during lunch and dinner. It’s a small family restaurant with around a 60-seat capacity. On average, 30 customers visit the restaurant at lunchtime and 40 for dinner. The typical lunch menu costs 10 euros and 20 euros for dinner. Therefore, they can garner the following revenues:
The restaurant can attain 27,500 euros in sales. Moreover, the owner, his wife, and four others work there. The three waiters make 2,000 euros each, and the chef makes 3,000—including social security contributions. So, the cost of personnel is 9,000 euros.
Food and drinks cost around one-third of the overall amount of sales. Therefore, the cost of goods sold amounts to 9,125 euros. Utility and other expenses are another 10%, which gives us an additional cost of 2,750 euros. The owners don’t pay rent because they own the restaurant. After calculations, the restaurant (before taxes) brings in a monthly profit of 6,625 euros.
For this estimate, let's take the world’s population to be 8 billion people. Out of those, assume that people between the ages of 12 and 65 use social media which we can approximate to account for 70% of the population. Let’s remove 10% more to account for people who either don’t have access to social media or have decided to not use one.
This would total to around 4.5 billion people regularly using social media. Next, we need to estimate the average time an individual spends on social media daily. This can vary widely by region, age group, and other factors. Averaging all those factors out, we can assume the average person spends about 2.5 hours per day on social media. Now, we multiply the total number of users by the average time spent:
Therefore, the estimated total number of hours spent on social media by all users worldwide in a single day, based on these assumptions, is 11 billion.
You can also gain insights into data science hiring processes by understanding how four of the world’s most prominent companies conduct data analyst interview questions and strategies.
Netflix conducts two detailed phone interviews with a recruiter and a hiring manager. Two onsite interviews are also given with around four data analyst team members. So, you can expect plenty of analytical, statistical (mostly A/B testing), and SQL programming and stats principles questions. You’ll likely be asked to analyze an assumed problem and identify key product management metrics. The second interview is with higher-level executives, with questions typically centered around the candidate’s background and professional experience.
LinkedIn’s interview process for hiring data analysts doesn’t differ much from other companies. They conduct phone screen interviews with SQL and Python questions and four to five onsite interviews. About half of the questions focus on advanced analytics, while the rest aim to assess your coding skills and statistical knowledge—e.g., Simpson’s paradox. Many data analyst interview questions are product-related and require a product mindset and quick thinking. You may also encounter inquiries about data applications and recommenders they use in their product.
Google’s data analyst interview process is relatively standard, with one or several phone screen interviews followed by onsite interviews. (Google has a guide for the technical part of the interview process that you can check out here.) The first phone screen is typically centered around technical data analyst questions. (Some candidates were also given an online SQL test.) Four to six people conduct the onsite interviews. All interviewers keep their notes confidential, so the possibility of bias in the interviewers’ feedback is low. The next step is to send the written feedback to a hiring committee, which then recommends it to Google executives for approval. Google’s hiring process can take longer than expected, so don’t hesitate to politely request a status update if a week or more has passed.
While Tesla’s data analyst interview questions may vary slightly among departments, the core requirements remain the same. Initially, you’ll receive a call from human resources to discuss your work experience and motivation. A second phone screen with a hiring manager may require you to answer technical questions about Python and SQL. You might also need to complete a 90-minute online SQL test, followed by a live Python test that lasts about an hour, where you’ll need to code in CoderPath.
If you get shortlisted, you'll attend an onsite panel interview, during which several senior members will ask back-to-back questions. The interview process can be lengthy, taking a few weeks to complete. Prepare to talk about your prior work experience and challenges—along with hands-on technical matters regarding optimization, SQL, Python, Tableau, and various scenarios of data wrangling. In this final rapid-fire round, you must demonstrate your knowledge, creativity, and ability to work in a team and under pressure. And while it's good to be patient, following up on your application might demonstrate your interest in the position.
Answering data analyst interview questions may initially be stressful. Take a page from our playbook if you feel challenged in the confidence department. Consider what we look for (and tips) when hiring expert data analysts to develop our 365 courses:
Lastly, if you don’t land the data analyst job, learn from your experience. Try performing mock data analyst interviews with a friend or colleague. Include the challenging data analyst interview questions you couldn’t answer and find a solution together. This will make you feel more self-assured in data analyst job interview questions. As Mark Meloon advises, "Chase fewer jobs but do a better job on them and do a post-mortem afterward so you can learn."
Good things happen when you consistently stay organized in your data analyst job search.
Are you ready for more data analyst interview questions?
If you need to enhance your data analyst interview preparation, you can gain valuable insights from the rigorous interview process of data scientists. And if you're looking to turn your interest in data science into a dedicated career, check out our course, Starting a Career in Data Science: Project Portfolio, Resume, and Interview Process.
You can also enroll in the 365 Data Analyst Career Track, allowing you to unlock your potential and providing solid preparation and lessons by industry-leading lecturers.